Predictive Control-Based Completeness Analysis and Global Calibration of Robot Vision Features

This paper provides an in-depth study and analysis of robot vision features for predictive control and a global calibration of their feature completeness. The acquisition and use of the complete macrofeature set are studied in the context of a robot task by defining the complete macrofeature set at the level of the overall purpose and constraints of the robot vision servo task. The visual feature set that can fully characterize the macropurpose and constraints of a vision servo task is defined as the complete macrofeature set. Due to the complexity of the task, a part of the features of the complete macrofeature set is obtained directly from the image, and another part of the features is obtained from the image by inference. The task is guaranteed to be completely based on a robust calibration-free visual serving strategy based on interference observer that is proposed to complete the visual serving task with high performance. To address the problems of singular values, local minima, and insufficient robustness in the traditional scale-free vision servo algorithm, a new scale-free vision servo method is proposed to construct a dual closed-loop vision servo structure based on interference observer, which ensures the closed-loop stability of the system through the Q-filter-based interference observer, while estimating and eliminating the interference consisting of hand-eye mapping model uncertainty and controlled robot input interference. The equivalent interference consisting of hand-eye mapping model uncertainty, controlled robot input interference, and detection noise is estimated and eliminated to obtain an inner-loop structure that presents a nominal model externally, and then an outer-loop controller is designed according to the nominal model to achieve the best performance of the system dynamic performance and robustness to optimally perform the vision servo task.

Bi-level optimization approach for robust mean-variance problems

Portfolio Optimization is based on the efficient allocation of several assets, which can get heavily affected by the uncertainty in input parameters. So we must look for such solutions which can give us steady results in uncertain conditions too. Recently, the uncertainty based optimization problems are being dealt with robust optimization approach. With this development, the interest of researchers has been shifted toward the robust portfolio optimization. In this paper, we study the robust counterparts of the uncertain mean-variance problems under box and ellipsoidal uncertainties. We convert those uncertain problems into bi-level optimization models and then derive their robust counterparts. We also solve a problem using this methodology and compared the optimal results of box and ellipsoidal uncertainty models with the nominal model.

Learning-based nonlinear model predictive control with accurate uncertainty compensation

A learning-based nonlinear model predictive control (LBNMPC) method is proposed in this paper for general nonlinear systems under system uncertainties and subject to state and input constraints. The proposed LBNMPC strategy decouples the robustness and performance requirements by employing an additional learned model and introducing it into the MPC framework along with the nominal model. The nominal model helps to ensure the closed-loop system’s safety and stability, and the learned model aims to improve the tracking behaviors. As a core of the learned model construction, an online parameter estimator is designed to deal with system uncertainties. This estimation process effectively evaluates both the current and historical effects of uncertainties, leading to superior estimating performance compared with conventional methods. By constructing an invariant terminal constraint set, we prove that the LBNMPC is recursively feasible and robustly asymptotically stable. Numerical verifications for a two-link manipulator are conducted to validate the effectiveness and robustness of the proposed control scheme.

Active Loading Control Design for a Wearable Exoskeleton with a Bowden Cable for Transmission

Exoskeletons with a Bowden cable for power transmission have the advantages of a concentrated mass and flexible movement. However, their integrated motor is disturbed by the Bowden cable’s friction, which limits the performance of the force loading response. In this paper, we solve this problem by designing an outer-loop feedforward-feedback proportion-differentiation controller based on an inner loop disturbance observer. Firstly, the inner loop’s dynamic performance is equivalent to the designed nominal model using the proposed disturbance observer, which effectively compensates for the parameter perturbation and friction disturbance. Secondly, based on an analysis of the stability of the inner loop controller, we obtain the stability condition and discuss the influence of modeling errors on the inner loop’s dynamic performance. Thirdly, to avoid excessive noise from the force sensors being introduced into the designed disturbance observer, we propose the feedforward-feedback proportion-differentiation controller based on the nominal model and pole configuration, which improves the outer loop’s force loading performance. Experiments are conducted, which verify the effectiveness of the proposed methods.

Accurate registration of point clouds of damaged aero-engine blades

This paper presents a novel method for aligning the scanned point clouds of damaged blades with their nominal CAD model. To inspect a damaged blade, the blade surface is scanned and the scan data in the form of a point cloud is compared to the nominal CAD model of the blade. To be able to compare the two surfaces, the scanned point cloud and the CAD model must be brought to the same coordinate system via a registration algorithm. The geometric nonconformity between the scanned point cloud and the nominal model stemmed from the damaged regions can affect the registration (alignment) outcome. The alignment errors then cause wrong inspection results. To prevent this from happening, the data points from the damaged regions have to be removed from the alignment calculations. The proposed registration method in this work can accurately and automatically eliminate the unreliable scanned data points of the damaged regions from the registration process. The main feature is a correspondence search technique based on the geometric properties of the local neighborhood of points. By combining the average curvature Hausdorff distance and average Euclidean Hausdorff distance, a metric is defined to locally measure the dissimilarities between the scan data and the nominal model and progressively remove the identified unreliable data points of the damaged regions with each iteration of the fine-tuned alignment algorithm. Implementation results have demonstrated that the proposed method is accurate and robust to noise with superior performance in comparison with the existing methods.

Two-Stage Robust Tracking Controller for Linear Systems With Known Uncertainty Using Filtered Basis Functions

There is growing interest in the use of the filtered basis functions (FBF) approach to track linear systems, especially nonminimum phase (NMP) plants, because of the distinct advantages it presents as compared to other popular methods in the literature. The FBF approach expresses the control input to the plant as a linear combination of basis functions. The basis functions are forward filtered through the plant dynamics and the coefficients of the linear combination are selected such that the tracking error is minimized. This paper proposes a two-stage robust filtered basis functions approach for tracking control of linear systems in the presence of known uncertainty. In the first stage, the nominal model for filtering the basis functions is selected such that a Frobenius norm metric which considers the known uncertainty is minimized. In the second stage, an optimal set of basis functions is selected such that the effect of uncertainty is minimized for the nominal model selected in the first stage. Experiments on a 3D printer, demonstrate up to 7 times improvement in tracking performance using the proposed method as compared to the standard FBF approach.

Gas-grain model of carbon fractionation in dense molecular clouds

ABSTRACT Carbon containing molecules in cold molecular clouds show various levels of isotopic fractionation through multiple observations. To understand such effects, we have developed a new gas-grain chemical model with updated 13C fractionation reactions (also including the corresponding reactions for 15 N, 18O, and 34S). For chemical ages typical of dense clouds, our nominal model leads to two 13C reservoirs: CO and the species that derive from CO, mainly s-CO and s-CH3OH, as well as C3 in the gas phase. The nominal model leads to strong enrichment in C3, c-C3H2, and C2H in contradiction with observations. When C3 reacts with oxygen atoms, the global agreement between the various observations and the simulations is rather good showing variable 13C fractionation levels that are specific to each species. Alternatively, hydrogen atom reactions lead to notable relative 13C fractionation effects for the two non-equivalent isotopologues of C2H, c-C3H2, and C2S. As there are several important fractionation reactions, some carbon bearing species are enriched in 13C, particularly CO, depleting atomic 13C in the gas phase. This induces a 13C depletion in CH4 formed on grain surfaces, an effect that is not observed in the CH4 in the Solar system, in particular on Titan. This seems to indicate a transformation of matter between the collapse of the molecular clouds, leading to the formation of the protostellar disc, and the formation of the planets. Or it means that the atomic carbon sticking to the grains reacts with the species already on the grains giving very little CH4.

Water transport throughout the TRAPPIST-1 system: the role of planetesimals

ABSTRACT Observational data suggest that a belt of planetesimals is expected close to the snow line in protoplanetary discs. Assuming there is such a belt in the TRAPPIST-1 system, we examine possibilities of water delivery to the planets via planetesimals from the belt. The study is accomplished by numerical simulations of dynamical evolution of a hypothetical planetesimal belt. Our results show that the inner part of the belt is dynamically unstable and planetesimals located in this region are quickly scattered away, with many of them entering the region around the planets. The main dynamical mechanism responsible for the instability are close encounters with the outermost planet Trappist-1h. A low-order mean-motion resonance 2:3 with Trappist-1h, located in the same region, also contributes to the objects transport. In our nominal model, the planets have received a non-negligible amount of water, with the smallest amount of 15 per cent of the current Earth’s water amount (EWA) being delivered to the planet 1b, while the planets Trappist-1e and Trappist-1g have received more than 60 per cent of the EWA. We have found that while the estimated efficiency of water transport to the planets is robust, the amount of water delivered to each planet may vary significantly, depending on the initial masses and orbits of the planets. The estimated dynamical ‘half-lives’ have shown that the impactors’ source region should be emptied in less then 1 Myr. Therefore, the obtained results suggest that the transport of planetesimals through the system preferably occurs during an early phase of the planetary system evolution.